Worldwide Analysis¶

The main goal of this analysis is to provide a visual and updated overview of the worldwide Covid-19 pandemic including:

Overall numbers in a nutshell.
Interactive worldwide map of active cases.
Details and tree map of the top fifteen countries with most active cases.
Time trends for top five countries by number of active cases.
Comparison of the situation of the top five countries with China and South Korea where the outbreak peak has passed and the number of active cases is still going down.

Data cleaning and first observations¶

Import the latest reports from CSSE at Johns Hopkins University. The datasets we will use are:

da_world: Worldwide data (time series).
da_confirmed_w: Number of confirmed cases per country (time series).
da_fatalities_w: Number of deaths per country (time series).
da_recovered_w: Number of recovered cases per country (time series).

Date of the latest data to be gathered is "yesterday" to ensure the data is available as the files are updated daily at midnight.
We use the date variable "yesterday" to build the urls dynamically.

First look at the overall data.

For our analysis we will use the following columns:

Country_Region
Last_Update
Lat
Long_
Confirmed
Deaths
Recovered
Active

Let's create a dataframe with these columns.

We rename the columns to the values originally used in this notebook as the column names from the sources have been changing over time.

Let's have a look at the nan values and fix them.

Country/Region     0
Last_Update        0
Latitude          73
Longitude         73
Confirmed          0
Deaths             0
Recovered          0
Active             0
dtype: int64

Let's list the countries without geographical coordinates.

All the nan values are in the columns Longute and Latitude.
First we create a list with the countries without geographical coordinates and the we define a function to add these values manually.

array(['US', 'Canada', 'Japan', 'Chile', 'Colombia', 'Germany', 'India',
       'Peru', 'Spain', 'United Kingdom', 'Diamond Princess',
       'MS Zaandam'], dtype=object)

Country/Region    0
Last_Update       0
Latitude          0
Longitude         0
Confirmed         0
Deaths            0
Recovered         0
Active            0
dtype: int64

We clean up the data and consolidate the names of countries with several variations or with a comma using the function format_country.
From the first look at the datasets, we found that South Korea is Korea,South in the original dataset and Congo is assigned for both the "Republic of the Congo" and the "Democratic Republic of the Congo" .

We add a column for the active positive cases.
Our main interest is to see how the numbers of active cases are changing.
Note: This column wasn't available in the original dataset when this notebook started to take shape.

Global numbers in a nutshell¶

Confirmed    7900924
Active       3698146
Recovered    3769712
Deaths        433066

We group the dataset by countries to have a total value per nation and list the top five countries with active cases.

Country/Region    0
Confirmed         0
Active            0
Recovered         0
Deaths            0
Latitude          0
Longitude         0
dtype: int64

Looking at the assigned coordinates, those countries with several entries in the dataset (e.g. autonomous territories within a country or countries with entries per state) may have assigned a point far from the capital city, e.g. for Denmark, the assigned coordinates were those of Greenland. We will adjust these points with the function add_coordinates.

We create a map where each country with active cases is labeled as follows:
Blue circle: less than 1000 reported active cases.
Orange circle: more than 1000 and less than 10000 reported active cases.
Red circle: more than 10000 reported active cases.

Summary data per country is shown if you click on each country's circle.

Top 15 countries by number of active cases¶

We list the fifteen countries with most active cases.
We also calculate the death rate as number of deaths over confirmed positive cases and include it in the column "Death rate [%]".
The value of death rate can be interpreted from several perspectives and it is a controversial value as each country has a different approach to count the numbers of deaths due to Covid-19, e.g. in Italy Covid-19 post-mortem tests are done while in Germany only deaths from people tested positive when alive are counted as Covid-19 deaths.

As of 28.04.20, Belgium has the highest death rate. It is to note that Belgium, unlike UK, Spain or Italy, include in the Covid-19 death toll fatalities outside hospitals as well as people suspected of having died of Covid-19.

As of 13.03.20, most of the victims Covid-19 positive in Italy were 70 years old and older 1.
According to a study by the Leverhulme Centre for Demographic Science at the University of Oxford 1 and a publication in FAZ 2, some of the main reasons for Italy's high death rate are:

Italy is the world's second oldest population after Japan with 23% of the population aged 65 years and older compared to 13.2% younger than 16 year old.
As of 26.03, the average age of cases in Italy is 63 years.
Northern Italy, the area in Italy with most cases, is the industrial heart of the country with high pollution levels and there are many inhabitants suffering from respiratory problems.
Family is very important in Italy and the contact between generations is close, even if multigenerations are not living during the same roof, they live close by and see each other frequently.
Hugs and kisses on the cheek are common when greeting people in Italy.
The initial lockdown measures in the Northern provices caused a boomerang effect as it caused an exodus and many students from the North with family in the South travelled home, contributing to the spread of the virus.

In contrast, Germany has a low death rate from the top-10 list of active cases even though it was on the fourth place of active cases as of 05.04. Possible factors that may influence this number are:

As of 01.04. Germany is the country that has executed most Covid-19 tests in the world in order to identify positive cases, trace them and contain spread. Italy is the second and South Korea the third. 3
Over 70% of the cases in Germany are in the age range between 15 and 59 years 4.
As of 26.03 the average age of cases is 45 years.
The elderly generation in Germany tends to be independent and not as in close contact with younger generations as in Italy or Spain.
The testing and tracing strategy that has been executed since the early days and the social structure in Germany of the outbreak might be the main reasons why the spread hasn't impacted the elderly population as badly as in Italy, keeping the death rate low.
Germany does not run Covid-19 post-mortem tests.

Summary data per country is shown if you point on each country's block.

Time trends for top five countries by number of active cases¶

We check the length of the datasets for worldwide confimed, recovered and fatalities and group them by country to avoid several entries per country.

Length of worldwide confirmed cases table: 266
Length of worldwide fatalities table: 266
Length of worldwide recovered table: 253

Length of unique country values for worldwide confirmed cases: 188
Length of unique country values for worldwide fatalities: 188
Length of unique country values for worldwide recovered: 188

China and South Korea are added to the list for comparison reasons as they have been the two first countries to slow down and decrease the number of active cases after an outbreak.

On 26.03, US has surpassed Italy in number of active cases and China in number of total positives.
US curve of active cases has grown dramatically since mid March.
Italy started a strong growth of cases around the carnival festivities in the third week of February whereas Germany's and Spain's curves of active cases started to go up at a fast pace around two weeks after. In Germany many of the initial cases were connected to people who went for a winter holiday in northern Italy in the third week of February.
As of 05.04, the growth of the active cases curves in the top European countries is showing signs of slowing down.
Question: Why did the curve in US take momentum in such a short timeframe in comparison to Italy, Germany and Spain? Might it be related to the lack of early contigency measures and lack of initial testing?
As in many of the countries with most active cases, the most dense and international cities are the ones worst hit by the outbreak. In US, the biggest hub of cases is located in NYC.

We compare the curves above with the one from China, the country with the first reported outbreak. It is worth mentioning that the official data provided by China has been questioned by the international community.
We added also the graph from South Korea where the active cases are starting to drop since mif March.
In both countries the peak of active cases shows in the graphs around a month since the curve started to increase at a fast pace.
Both countries took strict measures to content the spread of the virus including lockdowns of hotspots, social distancing, self-isolation and closing of public places and schools. In addition, in South Korea, a massive testing campaign was done to identify and traces cases in the earlier stages and limit the spread.
As of 15.06 the first wave of covid-19 cases has peaked in many Asian and European countries and restrictions in these countries have started to be eased. However, the risk of a second wave is latent as well as the development of out of control outbreaks in African countries as it is now in Brazil.

References¶

1 Jennifer Beam Dowd, Valentina Rotondi, Liliana Andriano, David M. Brazel, Per Block, Xuejie Ding, Yan Liu, Melinda C. Mills.
Demographic science aids in understanding the spread and fatality rates of COVID-19. March 15, 2020

2 Andreas Rossmann. Warum ist es in Italien so schlimm? Frankfurter Allgemeine Zeiting. March 23, 2020.

3 Frauke Suhr. So oft wird auf COVID-19 getestet. May 4, 2020.

4 Corona-Infektionen (COVID-19) in Deutschland nach Altersgruppen. May 17, 2020.

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Active	Combined_Key	Incidence_Rate	Case-Fatality_Ratio
0	45001.0	Abbeville	South Carolina	US	2020-06-15 03:33:14	34.223334	-82.461707	67	0	67	Abbeville, South Carolina, US	273.168345	0.000000
1	22001.0	Acadia	Louisiana	US	2020-06-15 03:33:14	30.295065	-92.414197	583	31	552	Acadia, Louisiana, US	939.640583	5.317324
2	51001.0	Accomack	Virginia	US	2020-06-15 03:33:14	37.767072	-75.632346	993	14	979	Accomack, Virginia, US	3072.781285	1.409869
3	16001.0	Ada	Idaho	US	2020-06-15 03:33:14	43.452658	-116.241552	892	22	870	Ada, Idaho, US	185.220947	2.466368
4	19001.0	Adair	Iowa	US	2020-06-15 03:33:14	41.330756	-94.471059	11	0	11	Adair, Iowa, US	153.803132	0.000000

	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Active
0	US	2020-06-15 03:33:14	34.223334	-82.461707	67	0	67
1	US	2020-06-15 03:33:14	30.295065	-92.414197	583	31	552
2	US	2020-06-15 03:33:14	37.767072	-75.632346	993	14	979
3	US	2020-06-15 03:33:14	43.452658	-116.241552	892	22	870
4	US	2020-06-15 03:33:14	41.330756	-94.471059	11	0	11

	Country/Region	Last_Update	Latitude	Longitude	Confirmed	Deaths	Recovered	Active
882	US	2020-06-15 03:33:14	NaN	NaN	164	5	0	159
1830	US	2020-06-15 03:33:14	NaN	NaN	4130	68	0	4062
2086	US	2020-06-15 03:33:14	NaN	NaN	2807	35	0	2772
2087	US	2020-06-15 03:33:14	NaN	NaN	12	0	0	12
2088	US	2020-06-15 03:33:14	NaN	NaN	119	1	0	118
2089	US	2020-06-15 03:33:14	NaN	NaN	47	0	0	47
2090	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2091	US	2020-06-15 03:33:14	NaN	NaN	463	7	0	456
2733	US	2020-06-15 03:33:14	NaN	NaN	380	5	0	375
2734	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2735	US	2020-06-15 03:33:14	NaN	NaN	0	2	0	-2
2736	US	2020-06-15 03:33:14	NaN	NaN	340	0	0	340
2737	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2738	US	2020-06-15 03:33:14	NaN	NaN	270	0	0	270
2739	US	2020-06-15 03:33:14	NaN	NaN	14	0	0	14
2740	US	2020-06-15 03:33:14	NaN	NaN	102	0	0	102
2741	US	2020-06-15 03:33:14	NaN	NaN	1675	3	0	1672
2742	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2743	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2744	US	2020-06-15 03:33:14	NaN	NaN	49	0	0	49
2745	US	2020-06-15 03:33:14	NaN	NaN	0	182	0	-182
2746	US	2020-06-15 03:33:14	NaN	NaN	5	0	0	5
2747	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2748	US	2020-06-15 03:33:14	NaN	NaN	11	0	0	11
2749	US	2020-06-15 03:33:14	NaN	NaN	293	113	0	180
2750	US	2020-06-15 03:33:14	NaN	NaN	2	0	0	2
2751	US	2020-06-15 03:33:14	NaN	NaN	0	32	0	-32
2752	US	2020-06-15 03:33:14	NaN	NaN	308	10	0	298
2753	US	2020-06-15 03:33:14	NaN	NaN	2	0	0	2
2754	US	2020-06-15 03:33:14	NaN	NaN	56	31	0	25
2755	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2756	US	2020-06-15 03:33:14	NaN	NaN	1	0	0	1
2757	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2758	US	2020-06-15 03:33:14	NaN	NaN	180	1	0	179
2759	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2760	US	2020-06-15 03:33:14	NaN	NaN	5	0	0	5
2761	US	2020-06-15 03:33:14	NaN	NaN	196	0	0	196
2762	US	2020-06-15 03:33:14	NaN	NaN	679	0	0	679
2763	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2764	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2765	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2766	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2767	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2768	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2769	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2770	US	2020-06-15 03:33:14	NaN	NaN	1870	833	0	1037
2771	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2772	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2773	US	2020-06-15 03:33:14	NaN	NaN	152	0	0	152
2774	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2775	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2776	US	2020-06-15 03:33:14	NaN	NaN	6	0	0	6
2777	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2778	US	2020-06-15 03:33:14	NaN	NaN	48	0	0	48
2779	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2780	US	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
2781	US	2020-06-15 03:33:14	NaN	NaN	0	17	0	-17
3178	Canada	2020-06-15 03:33:14	NaN	NaN	0	1	0	-1
3179	US	2020-06-15 03:33:14	NaN	NaN	49	0	0	49
3207	Canada	2020-06-15 03:33:14	NaN	NaN	13	0	13	0
3208	US	2020-06-15 03:33:14	NaN	NaN	103	3	0	100
3409	Japan	2020-06-15 03:33:14	NaN	NaN	256	0	170	86
3428	US	2020-06-15 03:33:14	NaN	NaN	0	0	561816	-630333
3522	Chile	2020-06-15 03:33:14	NaN	NaN	0	0	143704	-143704
3523	Colombia	2020-06-15 03:33:14	NaN	NaN	75	0	0	75
3524	Germany	2020-06-15 03:33:14	NaN	NaN	1199	0	0	1199
3525	India	2020-06-15 03:33:14	NaN	NaN	7436	0	0	7436
3526	Japan	2020-06-15 03:33:14	NaN	NaN	8	0	0	8
3527	Peru	2020-06-15 03:33:14	NaN	NaN	0	0	115579	-115579
3528	Spain	2020-06-15 03:33:14	NaN	NaN	0	0	0	0
3529	United Kingdom	2020-06-15 03:33:14	NaN	NaN	103470	0	0	103470
3614	Diamond Princess	2020-06-15 03:33:14	NaN	NaN	712	13	651	48
3666	MS Zaandam	2020-06-15 03:33:14	NaN	NaN	9	2	0	7

Country/Region	Confirmed	Active	Recovered	Deaths
US	2094058	1416510	561816	115732
Brazil	867624	355151	469141	43332
United Kingdom	297342	254276	1283	41783
Russia	528267	241793	279536	6938
India	320922	149348	162379	9195

	Country/Region	Confirmed	Active	Recovered	Deaths	Latitude	Longitude
0	US	2094058	1416510	561816	115732	NaN	NaN
1	Brazil	867624	355151	469141	43332	NaN	NaN
2	United Kingdom	297342	254276	1283	41783	NaN	NaN
3	Russia	528267	241793	279536	6938	NaN	NaN
4	India	320922	149348	162379	9195	NaN	NaN

	Country/Region	Last_Update	Latitude	Longitude	Confirmed	Deaths	Active
0	US	2020-06-15 03:33:14	38.907200	-77.036900	67	0	67
1	US	2020-06-15 03:33:14	38.907200	-77.036900	583	31	552
2	US	2020-06-15 03:33:14	38.907200	-77.036900	993	14	979
3	US	2020-06-15 03:33:14	38.907200	-77.036900	892	22	870
4	US	2020-06-15 03:33:14	38.907200	-77.036900	11	0	11
5	US	2020-06-15 03:33:14	38.907200	-77.036900	101	19	82
6	US	2020-06-15 03:33:14	38.907200	-77.036900	80	0	80
7	US	2020-06-15 03:33:14	38.907200	-77.036900	96	4	92
8	US	2020-06-15 03:33:14	38.907200	-77.036900	3743	144	3599
9	US	2020-06-15 03:33:14	38.907200	-77.036900	4	0	4
10	US	2020-06-15 03:33:14	38.907200	-77.036900	45	1	44
11	US	2020-06-15 03:33:14	38.907200	-77.036900	26	1	25
12	US	2020-06-15 03:33:14	38.907200	-77.036900	7	0	7
13	US	2020-06-15 03:33:14	38.907200	-77.036900	225	17	208
14	US	2020-06-15 03:33:14	38.907200	-77.036900	278	11	267
15	US	2020-06-15 03:33:14	38.907200	-77.036900	15	1	14
16	US	2020-06-15 03:33:14	38.907200	-77.036900	290	9	281
17	US	2020-06-15 03:33:14	38.907200	-77.036900	91	0	91
18	US	2020-06-15 03:33:14	38.907200	-77.036900	8	1	7
19	US	2020-06-15 03:33:14	38.907200	-77.036900	66	2	64
20	US	2020-06-15 03:33:14	38.907200	-77.036900	232	9	223
21	US	2020-06-15 03:33:14	38.907200	-77.036900	13	0	13
22	US	2020-06-15 03:33:14	38.907200	-77.036900	541	10	531
23	US	2020-06-15 03:33:14	38.907200	-77.036900	742	33	709
24	US	2020-06-15 03:33:14	38.907200	-77.036900	4320	112	4208
25	US	2020-06-15 03:33:14	38.907200	-77.036900	118	2	116
26	US	2020-06-15 03:33:14	38.907200	-77.036900	2020	118	1902
27	US	2020-06-15 03:33:14	38.907200	-77.036900	26	0	26
28	US	2020-06-15 03:33:14	38.907200	-77.036900	286	8	278
29	US	2020-06-15 03:33:14	38.907200	-77.036900	21	1	20
30	US	2020-06-15 03:33:14	38.907200	-77.036900	30	1	29
31	US	2020-06-15 03:33:14	38.907200	-77.036900	17	0	17
32	US	2020-06-15 03:33:14	38.907200	-77.036900	49	0	49
33	US	2020-06-15 03:33:14	38.907200	-77.036900	2151	48	2103
34	US	2020-06-15 03:33:14	38.907200	-77.036900	1	0	1
35	US	2020-06-15 03:33:14	38.907200	-77.036900	1	0	1
36	US	2020-06-15 03:33:14	38.907200	-77.036900	121	4	117
37	US	2020-06-15 03:33:14	38.907200	-77.036900	286	6	280
38	US	2020-06-15 03:33:14	38.907200	-77.036900	187	17	170
39	US	2020-06-15 03:33:14	38.907200	-77.036900	54	0	54

	Country/Region	Confirmed	Active	Recovered	Deaths	Latitude	Longitude
0	US	2094058	1416510	561816	115732	38.907200	-77.036900
1	Brazil	867624	355151	469141	43332	-9.023800	-70.812000
2	United Kingdom	297342	254276	1283	41783	51.507400	0.127800
3	Russia	528267	241793	279536	6938	44.693901	40.152042
4	India	320922	149348	162379	9195	20.593700	78.962900
5	Peru	229736	107469	115579	6688	-9.190000	-75.015200
6	France	194153	91761	72982	29410	4.000000	-53.000000
7	Pakistan	144478	88028	53721	2729	34.027401	73.947253
8	Bangladesh	87520	67619	18730	1171	23.685000	90.356300
9	Spain	243928	66416	150376	27136	40.463700	-3.749200
10	Sweden	51614	46740	0	4874	56.278400	15.018000
11	Netherlands	48990	42731	181	6078	12.521100	-69.968300
12	Saudi Arabia	127541	41849	84720	972	23.885942	45.079162
13	Belgium	60029	33785	16589	9655	50.833300	4.469936
14	Canada	100404	31518	60668	8218	45.421500	-75.697200
15	Egypt	44598	31092	11931	1575	26.820553	30.802498
16	South Africa	70038	30027	38531	1480	-30.559500	22.937500
17	Iran	187427	29916	148674	8837	32.427908	53.688046
18	Colombia	48896	27766	19460	1670	4.570900	-74.297300
19	Chile	174293	27266	143704	3323	-35.675100	-71.543000
20	Italy	236989	26274	176370	34345	42.351222	13.398438
21	Belarus	53973	23562	30103	308	53.709800	27.953400
22	Qatar	79602	22631	56898	73	25.354800	51.183900
23	Mexico	146837	22398	107298	17141	21.885300	-102.291600
24	Turkey	178239	22015	151417	4807	38.963700	35.243300
25	Indonesia	38277	21612	14531	2134	-0.789300	113.921300
26	Argentina	31577	21180	9564	833	-38.416100	-63.616700
27	Ecuador	46751	19791	23064	3896	-1.831200	-78.183400
28	Afghanistan	24766	19570	4725	471	33.939110	67.709953
29	Philippines	25930	18888	5954	1088	12.879721	121.774017
30	Ukraine	31851	16356	14596	899	49.444400	32.059800
31	Oman	23481	14923	8454	104	21.512583	55.923255
32	Bolivia	18459	14735	3113	611	-16.290200	-63.588700
33	United Arab Emirates	42294	14543	27462	289	23.424076	53.847818
34	Poland	29392	13919	14226	1247	51.919400	19.145100
35	Portugal	36690	12504	22669	1517	39.399900	-8.224500
36	Iraq	20209	11481	8121	607	33.223191	43.679291
37	Singapore	40604	10989	29589	26	1.283300	103.833300
38	Nigeria	16085	10445	5220	420	9.082000	8.675300
39	Armenia	16667	10184	6214	269	40.069100	45.038200

	Country/Region	Confirmed	Active	Recovered	Deaths	Latitude	Longitude
0	US	2094058	1416510	561816	115732	38.907200	-85.036900
1	Brazil	867624	355151	469141	43332	-14.235000	-51.925300
2	United Kingdom	297342	254276	1283	41783	51.507400	0.127800
3	Russia	528267	241793	279536	6938	44.693901	40.152042
4	India	320922	149348	162379	9195	20.593700	78.962900
5	Peru	229736	107469	115579	6688	-9.190000	-75.015200
6	France	194153	91761	72982	29410	48.856600	2.352200
7	Pakistan	144478	88028	53721	2729	34.027401	73.947253
8	Bangladesh	87520	67619	18730	1171	23.685000	90.356300
9	Spain	243928	66416	150376	27136	40.463700	-3.749200
10	Sweden	51614	46740	0	4874	56.278400	15.018000
11	Netherlands	48990	42731	181	6078	52.366700	4.894500
12	Saudi Arabia	127541	41849	84720	972	23.885942	45.079162
13	Belgium	60029	33785	16589	9655	50.833300	4.469936
14	Canada	100404	31518	60668	8218	45.421500	-75.697200
15	Egypt	44598	31092	11931	1575	26.820553	30.802498
16	South Africa	70038	30027	38531	1480	-30.559500	22.937500
17	Iran	187427	29916	148674	8837	32.427908	53.688046
18	Colombia	48896	27766	19460	1670	4.570900	-74.297300
19	Chile	174293	27266	143704	3323	-35.675100	-71.543000
20	Italy	236989	26274	176370	34345	42.351222	13.398438
21	Belarus	53973	23562	30103	308	53.709800	27.953400
22	Qatar	79602	22631	56898	73	25.354800	51.183900
23	Mexico	146837	22398	107298	17141	21.885300	-102.291600
24	Turkey	178239	22015	151417	4807	38.963700	35.243300
25	Indonesia	38277	21612	14531	2134	-0.789300	113.921300
26	Argentina	31577	21180	9564	833	-38.416100	-63.616700
27	Ecuador	46751	19791	23064	3896	-1.831200	-78.183400
28	Afghanistan	24766	19570	4725	471	33.939110	67.709953
29	Philippines	25930	18888	5954	1088	12.879721	121.774017
30	Ukraine	31851	16356	14596	899	49.444400	32.059800
31	Oman	23481	14923	8454	104	21.512583	55.923255
32	Bolivia	18459	14735	3113	611	-16.290200	-63.588700
33	United Arab Emirates	42294	14543	27462	289	23.424076	53.847818
34	Poland	29392	13919	14226	1247	51.919400	19.145100
35	Portugal	36690	12504	22669	1517	39.399900	-8.224500
36	Iraq	20209	11481	8121	607	33.223191	43.679291
37	Singapore	40604	10989	29589	26	1.283300	103.833300
38	Nigeria	16085	10445	5220	420	9.082000	8.675300
39	Armenia	16667	10184	6214	269	40.069100	45.038200

	Country/Region	Confirmed	Active	Recovered	Deaths	Latitude	Longitude	Death rate [%]
0	US	2094058	1416510	561816	115732	38.907200	-85.036900	5.53
1	Brazil	867624	355151	469141	43332	-14.235000	-51.925300	4.99
2	United Kingdom	297342	254276	1283	41783	51.507400	0.127800	14.05
3	Russia	528267	241793	279536	6938	44.693901	40.152042	1.31
4	India	320922	149348	162379	9195	20.593700	78.962900	2.87
5	Peru	229736	107469	115579	6688	-9.190000	-75.015200	2.91
6	France	194153	91761	72982	29410	48.856600	2.352200	15.15
7	Pakistan	144478	88028	53721	2729	34.027401	73.947253	1.89
8	Bangladesh	87520	67619	18730	1171	23.685000	90.356300	1.34
9	Spain	243928	66416	150376	27136	40.463700	-3.749200	11.12
10	Sweden	51614	46740	0	4874	56.278400	15.018000	9.44
11	Netherlands	48990	42731	181	6078	52.366700	4.894500	12.41
12	Saudi Arabia	127541	41849	84720	972	23.885942	45.079162	0.76
13	Belgium	60029	33785	16589	9655	50.833300	4.469936	16.08
14	Canada	100404	31518	60668	8218	45.421500	-75.697200	8.18